11 research outputs found

    Persian Heritage Image Binarization Competition (PHIBC 2012)

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    The first competition on the binarization of historical Persian documents and manuscripts (PHIBC 2012) has been organized in conjunction with the first Iranian conference on pattern recognition and image analysis (PRIA 2013). The main objective of PHIBC 2012 is to evaluate performance of the binarization methodologies, when applied on the Persian heritage images. This paper provides a report on the methodology and performance of the three submitted algorithms based on evaluation measures has been used.Comment: 4 pages, 2 figures, conferenc

    Efficient and effective objective image quality assessment metrics

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    Acquisition, transmission, and storage of images and videos have been largely increased in recent years. At the same time, there has been an increasing demand for high quality images and videos to provide satisfactory quality-of-experience for viewers. In this respect, high dynamic range (HDR) imaging with higher than 8-bit depth has been an interesting approach in order to capture more realistic images and videos. Objective image and video quality assessment plays a significant role in monitoring and enhancing the image and video quality in several applications such as image acquisition, image compression, multimedia streaming, image restoration, image enhancement and displaying. The main contributions of this work are to propose efficient features and similarity maps that can be used to design perceptually consistent image quality assessment tools. In this thesis, perceptually consistent full-reference image quality assessment (FR-IQA) metrics are proposed to assess the quality of natural, synthetic, photo-retouched and tone-mapped images. In addition, efficient no-reference image quality metrics are proposed to assess JPEG compressed and contrast distorted images. Finally, we propose a perceptually consistent color to gray conversion method, perform a subjective rating and evaluate existing color to gray assessment metrics. Existing FR-IQA metrics may have the following limitations. First, their performance is not consistent for different distortions and datasets. Second, better performing metrics usually have high complexity. We propose in this thesis an efficient and reliable full-reference image quality evaluator based on new gradient and color similarities. We derive a general deviation pooling formulation and use it to compute a final quality score from the similarity maps. Extensive experimental results verify high accuracy and consistent performance of the proposed metric on natural, synthetic and photo retouched datasets as well as its low complexity. In order to visualize HDR images on standard low dynamic range (LDR) displays, tone-mapping operators are used in order to convert HDR into LDR. Given different depth bits of HDR and LDR, traditional FR-IQA metrics are not able to assess the quality of tone-mapped images. The existing full-reference metric for tone-mapped images called TMQI converts both HDR and LDR to an intermediate color space and measure their similarity in the spatial domain. We propose in this thesis a feature similarity full-reference metric in which local phase of HDR is compared with the local phase of LDR. Phase is an important information of images and previous studies have shown that human visual system responds strongly to points in an image where the phase information is ordered. Experimental results on two available datasets show the very promising performance of the proposed metric. No-reference image quality assessment (NR-IQA) metrics are of high interest because in the most present and emerging practical real-world applications, the reference signals are not available. In this thesis, we propose two perceptually consistent distortion-specific NR-IQA metrics for JPEG compressed and contrast distorted images. Based on edge statistics of JPEG compressed images, an efficient NR-IQA metric for blockiness artifact is proposed which is robust to block size and misalignment. Then, we consider the quality assessment of contrast distorted images which is a common distortion. Higher orders of Minkowski distance and power transformation are used to train a low complexity model that is able to assess contrast distortion with high accuracy. For the first time, the proposed model is used to classify the type of contrast distortions which is very useful additional information for image contrast enhancement. Unlike its traditional use in the assessment of distortions, objective IQA can be used in other applications. Examples are the quality assessment of image fusion, color to gray image conversion, inpainting, background subtraction, etc. In the last part of this thesis, a real-time and perceptually consistent color to gray image conversion methodology is proposed. The proposed correlation-based method and state-of-the-art methods are compared by subjective and objective evaluation. Then, a conclusion is made on the choice of the objective quality assessment metric for the color to gray image conversion. The conducted subjective ratings can be used in the development process of quality assessment metrics for the color to gray image conversion and to test their performance

    A Set of Criteria for Face Detection Preprocessing

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    AbstractThe goal of this paper is to provide a robust set of preprocessing steps to be used with any face detection system. Usually, the purpose of using preprocessing steps in face detection system is to speed up the detection process and reducing false positives. A preprocessing step should reject an acceptable amount of non-face windows. First proposed criterion is based on linear image transform (LIT) which ignores scanning a number of non-face windows. Second criterion utilizes regional minima (RM) to reject non-face windows. The last one uses a modified adaptive thresholding (ADT) technique to convert input image into a binary representation and perform an exclusion process on the latter form. The proposed criteria have been used in conjunction with a version of Viola-Jones face detector. Experimental results show significant advantage against early exclusion criterion or variance classifier in terms of speed and rejection rate. CMU-MIT and BioID datasets have been used in the experiments

    Automatic Document Image Binarization using Bayesian Optimization

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    Document image binarization is often a challenging task due to various forms of degradation. Although there exist several binarization techniques in literature, the binarized image is typically sensitive to control parameter settings of the employed technique. This paper presents an automatic document image binarization algorithm to segment the text from heavily degraded document images. The proposed technique uses a two band-pass filtering approach for background noise removal, and Bayesian optimization for automatic hyperparameter selection for optimal results. The effectiveness of the proposed binarization technique is empirically demonstrated on the Document Image Binarization Competition (DIBCO) and the Handwritten Document Image Binarization Competition (H-DIBCO) datasets

    Phase-Based Binarization of Ancient Document Images: Model and Applications

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    CorrC2G: Color to Gray Conversion by Correlation

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